Summary of Simrag: Self-improving Retrieval-augmented Generation For Adapting Large Language Models to Specialized Domains, by Ran Xu et al.
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
by Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes SimRAG, a self-training approach for large language models (LLMs) to adapt to specialized fields like science and medicine. The goal is to enhance question-answering (QA) abilities while addressing challenges from distribution shifts and limited domain-specific data. The method fine-tunes the LLM on instruction-following, QA, and search-related data, then prompts it to generate diverse questions from unlabeled corpora. This self-generated synthetic data is used to improve performance on domain-specific RAG tasks. Experiments on 11 datasets across two backbone sizes and three domains show that SimRAG outperforms baselines by 1.2%–8.6%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SimRAG helps computers better understand questions and answer them correctly, especially in fields like science and medicine where data is limited. The system uses a self-training approach to adapt to new areas of study. It starts by teaching the computer to follow instructions and answer questions, then asks it to generate new questions based on what it has learned. This helps the computer learn more about the new field and improve its ability to answer questions. Overall, SimRAG is an important step towards making computers better at understanding and answering complex questions. |
Keywords
» Artificial intelligence » Question answering » Rag » Self training » Synthetic data
